This training is designed for software engineers, AI engineers, platform developers, and technical leaders who want to develop production-grade AI agents using Anthropic's Claude Agent SDK. At the heart of the program is the following approach: learning the Claude Agent SDK is not simply "calling an API for chat." Real engineering value comes from selecting the right agent architecture, grounding tool-definition and schema design on a solid foundation, exposing enterprise tools to the agent via the Model Context Protocol (MCP), coordinating complex work streams via sub-agent and multi-agent patterns, applying policy enforcement through hook-based governance, controlling cost via prompt caching and model routing, measuring behavior with an evaluation framework, and binding all of this to an observable, deployable production system. For this reason, the training addresses SDK usage, architectural decisions, MCP integration, multi-agent coordination, hook orchestration, cost optimization, evaluation, and production deployment together.
The training positions the Claude Agent SDK in context within the agentic AI ecosystem. A head-to-head comparison is made with alternatives like LangChain, LangGraph, AutoGen, CrewAI, and the OpenAI Agents SDK; the strengths and weaknesses of each tool, Anthropic's "minimal but composable" philosophy, and the problem classes in which the Claude Agent SDK is the stronger choice are explained from an architectural perspective. The core motto of the program is: "The Claude Agent SDK does not offer you an agent framework; it offers a simple, powerful engineering tool that lets you directly control model behavior to build agents." For this reason, the training neither hides behind high-level abstractions nor drowns in unnecessary low-level boilerplate; instead, it teaches the systematic engineering discipline of shaping agent behavior through the direct control the SDK provides.
One of the strengths of the program is that it addresses Anthropic's 2026 ecosystem standards — Model Context Protocol (MCP), Sub-agents, Hooks, Extended Thinking, Prompt Caching — through hands-on, real production scenarios. Participants learn how to create production-grade project scaffolds in the TypeScript and Python SDKs, apply input_schema and description-optimization techniques for tool definition, design parallel tool calling and fault-tolerant tool-execution patterns, keep agent behavior grounded with system-prompt architecture, build a conversation-persistence layer on Postgres / Redis / vector stores, integrate Jira / Postgres / internal APIs via MCP, and design multi-agent systems with sub-agent / supervisor / orchestrator-worker / mesh patterns.
This training proceeds at a depth different from traditional "ChatGPT API usage" courses. Advanced topics are covered comprehensively: correctly consuming streaming responses in the TypeScript and Python SDKs, tuning the extended-thinking budget for the task, supporting multi-step reasoning via interleaved thinking, enforcing policy through hook-based pre-tool-use and post-tool-use filters, applying data-classification controls via secret scanning and PII masking, and establishing human-in-the-loop approval flows with Slack/Teams. Cost-optimization techniques like up to 90% token savings via prompt caching, processing async workloads at a 50% discount via the batch API, and task-aware model routing among Haiku 4.5 / Sonnet 4.6 / Opus 4.7 are also covered hands-on.
Another critical dimension of the program is the evaluation-engineering discipline. Taking an agent to production is not just developing and deploying it; it is systematically measuring its behavior and protecting it from regressions. The training covers test set and rubric design, automated scoring via LLM-as-judge patterns, judge-prompt engineering and bias control, inter-rater agreement and human-eval calibration, regression-test pipelines with CI/CD integration, and red teaming against prompt-injection and jailbreak scenarios. On the production-deployment side, stateful agent-service deployment patterns on Vercel, AWS (Lambda, Fargate, ECS), GCP, and Kubernetes; OpenTelemetry / Langfuse / Helicone integrations; conversation-level and tool-level telemetry; reproducible incident analysis via structured logging; and operational topics like agent rollback and blue-green deployment are covered.
Although Turkey leads global ChatGPT traffic at 94.49%, the Anthropic Claude ecosystem is still an immature area in terms of Turkish-language resources. Turkish content on production-grade agent development with the Claude Agent SDK is virtually nonexistent; this training is designed as the most comprehensive reference program filling this gap with architectural depth and a hands-on engineering perspective. As a capstone project, participants develop a custom end-to-end AI agent tailored to their own enterprise use case, measure its behavior with an evaluation framework, optimize costs via prompt caching, integrate an observability layer, and finally present a production-ready system.
The training consists of 4 days, 12 modules, and over 80 lessons; each module is supported by a theoretical framework, architectural decision sets, and real enterprise use cases. By the end of the program, participants will have the technical competence to develop custom agents at the SDK level, build MCP servers, coordinate multi-agent systems, perform cost-aware production deployment via prompt caching and model routing, and continuously measure agent quality through the evaluation-engineering discipline. The training is designed with a scope suitable both for startup AI teams seeking fast time-to-market and for enterprise AI engineering teams establishing a scalable agent platform.